One of the advantages of spectral imaging, such as 2D or 3D x-ray imaging, for instance spectral Computed Tomography (CT), is that it may provide quantitative material images after material decomposition. For the material decomposition various methods may be contemplated, including, for instance, a dimensional look-up table approach (for instance known from Alvarez, R. E, Estimator for photon counting energy selective x-ray imaging with multi-bin pulse height analysis, Med. Phys., 2011, 38, 2324-2334), a theoretical forward model (for instance known from Roessl, E., Proksa, R, K-edge imaging in x-ray computed tomography using multi-bin photon counting detectors, Phys. Med. Biol., 2007, 52, 4679-4696) or application of a semi-analytic approach (for instance known from Schirra, C. et al, Towards In-vivo K-edge Imaging Using a New Semi-Analytical Calibration Method, Proc. SPIE 9033, Medical Imaging 2014: Physics of Medical Imaging, 90330N, 19 Mar. 2014).
These and other known methods face the same problem that the outcome becomes distorted in case the status of the scanner and object of interest differ from the status during calibration. The status of the detector may change due to polarization of the sensor or the x-ray spectrum from the tube might change, e.g. due to heating during scanning An approach utilizing a look-up table (even with three or more materials) suffers from the fact that the human body does not consist exactly out of the materials used in the calibration. Artefacts like rings, bands, or cross-talk between the material images occurs due to the energy dependency of the attenuation of different materials, the different detector spectral responses and x-ray spectrum for different detector parts. It is likely that spectral forward models or semi-analytical approach never exactly matches the measurement. Scatter has a spectral footprint and changes the detected counts differently in different bins. This complicates the model even further. The mismatch in prediction and measurement produces cross-talk and artefacts in the material images. A better forward model or semi-analytical approach reduces the artefacts but it is not clear if the ultimately resulting images quality is good enough for quantitative medical imaging.
Besides the previously described systematic errors, the quality of the (temporal) stability of detector might frequently make a new spectral calibration necessary. It would be desirable to assess the status of the CT scanner in order to recommend a spectral calibration.
All mentioned image artefacts depend on the chosen scan protocols, especially on the x-ray tube voltage. After best processing, it would be preferable to provide a user, such as an operator or a physician, in a qualitative and a quantitative way about the remaining errors in the images. In best case the user is informed about the absence of significant errors.
US2012/0155617 A1 discloses a spectral CT calibration phantom that has inserts which allow for filling with a liquid) material that mimic the attenuation of different parts of a body.
WO2008/046498 A1 discloses a calibration method for two or more spectra tomography to determine material decomposition coefficients.